Computer Vision
Efficient UAV trajectory prediction: A multi-modal deep diffusion framework
Yuan Gao, Xinyu Guo, Wenjing Xie et al. (+4 more)
To meet the requirements for managing unauthorized
UAVs in the low-altitude economy, a multi-modal
UAV trajectory prediction method based on the fusion of
LiDAR and millimeter-wave radar information is proposed. A deep fusion network for multi-modal
UAV trajectory prediction, termed the Multi-Modal Deep Fusion Framework, is designed. The overall architecture consists of two modality-specific feature extraction networks and a bidirectional cross-
attention fusion module, aiming to fully exploit the complementary information of
LiDAR and radar point clouds in spatial
geometric structure and dynamic reflection characteristics. In the feature extraction stage, the model employs independent but structurally identical feature encoders for
LiDAR and radar. After feature extraction, the model enters the Bidirectional Cross-
Attention Mechanism stage to achieve information complementarity and semantic
alignment between the two modalities. To verify the effectiveness of the proposed model, the MMAUD
dataset used in the CVPR 2024 UG2+
UAV Tracking and Pose-Estimation Challenge is adopted as the training and testing
dataset. Experimental results show that the proposed multi-modal fusion model significantly improves trajectory prediction accuracy, achieving a 40% improvement compared to the baseline model. In addition, ablation experiments are conducted to demonstrate the effectiveness of different loss functions and post-processing strategies in improving model performance. The proposed model can effectively utilize multi-modal data and provides an efficient solution for unauthorized
UAV trajectory prediction in the low-altitude economy.
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All authors Yuan Gao, Xinyu Guo, Wenjing Xie, Zifan Wang, Hongwen Yu, Gongyang Li, Shugong Xu
Computer Vision
NC-Reg : Neural Cortical Maps for Rigid Registration
Ines Vati, Pierrick Bourgeat, Rodrigo Santa Cruz et al. (+4 more)
We introduce neural cortical maps, a continuous and compact neural representation for cortical feature maps, as an alternative to traditional discrete structures such as grids and meshes. It can learn from meshes of arbitrary size and provide learnt features at any resolution. Neural cortical maps enable efficient optimization on the sphere and achieve runtimes up to 30 times faster than classic barycentric interpolation (for the same number of iterations). As a proof of concept, we investigate rigid registration of cortical surfaces and propose NC-Reg, a novel iterative
algorithm that involves the use of neural cortical feature maps, gradient descent optimization and a simulated annealing strategy. Through ablation studies and subject-to-template experiments, our method demonstrates sub-degree accuracy ($<1^\circ$ from the global optimum), and serves as a promising robust pre-
alignment strategy, which is critical in clinical settings.
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All authors Ines Vati, Pierrick Bourgeat, Rodrigo Santa Cruz, Vincent Dore, Olivier Salvado, Clinton Fookes, Léo Lebrat
Computer Vision
Non-Invasive 3D Wound Measurement with RGB-D Imaging
Lena Harkämper, Leo Lebrat, David Ahmedt-Aristizabal et al. (+3 more)
Chronic wound monitoring and management require accurate and efficient wound measurement methods. This paper presents a fast, non-invasive 3D wound measurement
algorithm based on RGB-D imaging. The method combines RGB-D odometry with B-spline surface reconstruction to generate detailed 3D wound meshes, enabling automatic computation of clinically relevant wound measurements such as perimeter, surface area, and dimensions. We evaluated our system on realistic silicone wound phantoms and measured sub-millimetre 3D reconstruction accuracy compared with high-resolution ground-truth scans. The extracted measurements demonstrated low variability across repeated captures and strong agreement with manual assessments. The proposed pipeline also outperformed a state-of-the-art object-centric RGB-D reconstruction method while maintaining runtimes suitable for real-time clinical deployment. Our approach offers a promising tool for automated wound assessment in both clinical and remote healthcare settings.
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All authors Lena Harkämper, Leo Lebrat, David Ahmedt-Aristizabal, Olivier Salvado, Mattias Heinrich, Rodrigo Santa Cruz
Computer Vision
Anatomically-aware conformal prediction for medical image segmentation with random walks
Mélanie Gaillochet, Christian Desrosiers and Hervé Lombaert
The reliable deployment of deep learning in medical imaging requires uncertainty quantification that provides rigorous error guarantees while remaining anatomically meaningful. Conformal prediction (CP) is a powerful distribution-free framework for constructing statistically valid prediction intervals. However, standard applications in
segmentation often ignore anatomical context, resulting in fragmented, spatially incoherent, and over-segmented prediction sets that limit clinical utility. To bridge this gap, this paper proposes Random-Walk Conformal Prediction (RW-CP), a model-agnostic framework which can be added on top of any
segmentation method. RW-CP enforces spatial coherence to generate anatomically valid sets. Our method constructs a k-nearest neighbour graph from pre-trained
vision foundation model features and applies a random walk to diffuse uncertainty. The random walk
diffusion regularizes the non-conformity scores, making the prediction sets less sensitive to the conformal calibration parameter $\lambda$, ensuring more stable and continuous anatomical boundaries. RW-CP maintains rigorous marginal coverage while significantly improving
segmentation quality. Evaluations on multi-modal public
datasets show improvements of up to $35.4\%$ compared to standard CP baselines, given an allowable error rate of $\alpha=0.1$.
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Computer Vision
FreeOrbit4D: Training-Free Arbitrary Camera Redirection for Monocular Videos via Foreground-Complete 4D Reconstruction
Wei Cao, Hao Zhang, Fengrui Tian et al. (+5 more)
Camera redirection aims to replay a dynamic scene from a single
monocular video under a user-specified camera trajectory. However, large-angle redirection is inherently ill-posed: a
monocular video captures only a narrow spatio-temporal view of a dynamic 3D scene, providing severely limited observations of the underlying 4D world. The key challenge is therefore to recover a complete and coherent representation from this limited input, with consistent geometry and motion. While recent
diffusion-based methods achieve impressive visual generation quality, they often break down under large-angle viewpoint changes far from the original trajectory, where missing visual grounding leads to severe
geometric ambiguity and temporal inconsistency. We present FreeOrbit4D, an effective training-free framework that tackles this ambiguity by recovering a foreground-complete 4D proxy as structural grounding for
video generation. We obtain this proxy by decoupling foreground and background reconstructions: we unproject the
monocular video into a static background and partial foreground point clouds in a unified global space, then use an object-centric multi-view
diffusion model to synthesize multi-view images and reconstruct complete foreground point clouds in canonical object space. By aligning the canonical foreground point cloud to the global scene space via dense pixel-synchronized 3D-3D correspondences and projecting the foreground-complete 4D proxy onto target camera viewpoints, we provide
geometric scaffolds that guide a conditional
video diffusion model. Extensive experiments show that FreeOrbit4D produces more faithful and temporally coherent redirected
videos under challenging large-angle trajectories, and our proxy further enables applications such as edit propagation and 4D data generation. Project page: https://freeorbit4d.
vision.ischool.illinois.edu/
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All authors Wei Cao, Hao Zhang, Fengrui Tian, Yulun Wu, Yingying Li, Shenlong Wang, Ning Yu, Yaoyao Liu
Computer Vision
Pay Attention to Where You Looked
Alex Berian, JhihYang Wu, Daniel Brignac et al. (+2 more)
Novel view synthesis (NVS) has advanced with generative modeling, enabling photorealistic image generation. In few-shot NVS, where only a few input views are available, existing methods often assume equal importance for all input views relative to the target, leading to suboptimal results. We address this limitation by introducing a camera-weighting mechanism that adjusts the importance of source views based on their relevance to the target. We propose two approaches: a deterministic weighting scheme leveraging
geometric properties like Euclidean distance and angular differences, and a cross-
attention-based learning scheme that optimizes view weighting. Additionally, models can be further trained with our camera-weighting scheme to refine their understanding of view relevance and enhance synthesis quality. This mechanism is adaptable and can be integrated into various NVS
algorithms, improving their ability to synthesize high-quality novel views. Our results demonstrate that adaptive view weighting enhances accuracy and realism, offering a promising direction for improving NVS.
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All authors Alex Berian, JhihYang Wu, Daniel Brignac, Natnael Daba, Abhijit Mahalanobis
Computer Vision
Smart Split-Federated Learning over Noisy Channels for Embryo Image Segmentation
Zahra Hafezi Kafshgari, Ivan V. Bajic and Parvaneh Saeedi
Split-
Federated (SplitFed) learning is an extension of
federated learning that places minimal requirements on the clients computing infrastructure, since only a small portion of the overall model is deployed on the clients hardware. In SplitFed learning, feature values, gradient updates, and model updates are transferred across communication channels. In this paper, we study the effects of noise in the communication channels on the learning process and the quality of the final model. We propose a smart averaging strategy for SplitFed learning with the goal of improving resilience against channel noise. Experiments on a
segmentation model for embryo images shows that the proposed smart averaging strategy is able to tolerate two orders of magnitude stronger noise in the communication channels compared to conventional averaging, while still maintaining the accuracy of the final model.
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Computer Vision
On the Role of Depth in Surgical Vision Foundation Models: An Empirical Study of RGB-D Pre-training
John J. Han, Adam Schmidt, Muhammad Abdullah Jamal et al. (+4 more)
Vision foundation models (VFMs) have emerged as powerful tools for
surgical scene understanding. However, current approaches predominantly rely on unimodal RGB pre-training, overlooking the complex 3D geometry inherent to
surgical environments. Although several architectures support
multimodal or geometry-aware inputs in general computer
vision, the benefits of incorporating depth information in
surgical settings remain underexplored. We conduct a large-scale empirical study comparing eight ViT-based VFMs that differ in pre-training domain, learning objective, and input modality (RGB vs. RGB-D). For pre-training, we use a curated
dataset of 1.4 million robotic
surgical images paired with depth maps generated from an off-the-shelf network. We evaluate these models under both frozen-backbone and end-to-end fine-tuning protocols across eight
surgical datasets spanning object detection,
segmentation, depth estimation, and pose estimation. Our experiments yield several consistent findings. Models incorporating explicit
geometric tokenization, such as MultiMAE, substantially outperform unimodal baselines across all tasks. Notably,
geometric-aware pre-training enables remarkable data efficiency: models fine-tuned on just 25% of labeled data consistently surpass RGB-only models trained on the full
dataset. Importantly, these gains require no architectural or runtime changes at inference; depth is used only during pre-training, making adoption straightforward. These findings suggest that
multimodal pre-training offers a viable path towards building more capable
surgical vision systems.
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All authors John J. Han, Adam Schmidt, Muhammad Abdullah Jamal, Chinedu Nwoye, Anita Rau, Jie Ying Wu, Omid Mohareri
Computer Vision · Cross-listed from cs.RO
DeFM: Learning Foundation Representations from Depth for Robotics
Manthan Patel, Jonas Frey, Mayank Mittal et al. (+5 more)
Depth sensors are widely deployed across robotic platforms, and advances in fast, high-fidelity depth simulation have enabled robotic policies trained on depth observations to achieve robust sim-to-real transfer for a wide range of tasks. Despite this, representation learning for depth modality remains underexplored compared to RGB, where large-scale foundation models now define the state of the art. To address this gap, we present DeFM, a self-supervised foundation model trained entirely on depth images for robotic applications. Using a DINO-style self-
distillation objective on a curated
dataset of 60M depth images, DeFM learns
geometric and semantic representations that generalize to diverse environments, tasks, and sensors. To retain metric awareness across multiple scales, we introduce a novel input normalization strategy. We further distill DeFM into compact models suitable for resource-constrained robotic systems. When evaluated on depth-based classification,
segmentation,
navigation, locomotion, and manipulation
benchmarks, DeFM achieves state-of-the-art performance and demonstrates strong generalization from simulation to real-world environments. We release all our pretrained models, which can be adopted off-the-shelf for depth-based robotic learning without task-specific fine-tuning. Webpage: https://de-fm.github.io/
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All authors Manthan Patel, Jonas Frey, Mayank Mittal, Fan Yang, Alexander Hansson, Amir Bar, Cesar Cadena, Marco Hutter
Computer Vision
RealStats: A Rigorous Real-Only Statistical Framework for Fake Image Detection
Haim Zisman and Uri Shaham
As generative models continue to evolve, detecting AI-generated images remains a critical challenge. While effective detection methods exist, they often lack formal interpretability and may rely on implicit assumptions about fake content, potentially limiting robustness to distributional shifts. In this work, we introduce a rigorous, statistically grounded framework for fake image detection that focuses on producing a probability score interpretable with respect to the real-image population. Our method leverages the strengths of multiple existing detectors by combining training-free statistics. We compute p-values over a range of test statistics and aggregate them using classical statistical ensembling to assess
alignment with the unified real-image distribution. This framework is generic, flexible, and training-free, making it well-suited for robust fake image detection across diverse and evolving settings.
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Computer Vision
Weakly supervised framework for wildlife detection and counting in challenging Arctic environments: a case study on caribou (Rangifer tarandus)
Ghazaleh Serati, Samuel Foucher and Jerome Theau
Caribou across the Arctic has declined in recent decades, motivating scalable and accurate monitoring approaches to guide evidence-based conservation actions and policy decisions. Manual interpretation from this imagery is labor-intensive and error-prone, underscoring the need for automatic and reliable detection across varying scenes. Yet, such automatic detection is challenging due to severe background heterogeneity, dominant empty terrain (class imbalance), small or occluded targets, and wide variation in density and scale. To make the detection model (HerdNet) more robust to these challenges, a weakly supervised patch-level pretraining based on a detection network's architecture is proposed. The detection
dataset includes five caribou herds distributed across Alaska. By learning from empty vs. non-empty labels in this
dataset, the approach produces early weakly supervised knowledge for enhanced detection compared to HerdNet, which is initialized from generic weights. Accordingly, the patch-based pretrain network attained high accuracy on multi-herd imagery (2017) and on an independent year's (2019) test sets (F1: 93.7%/92.6%, respectively), enabling reliable mapping of regions containing animals to facilitate manual counting on large aerial imagery. Transferred to detection, initialization from weakly supervised pretraining yielded consistent gains over ImageNet weights on both positive patches (F1: 92.6%/93.5% vs. 89.3%/88.6%), and full-image counting (F1: 95.5%/93.3% vs. 91.5%/90.4%). Remaining limitations are false positives from animal-like background clutter and false negatives related to low animal density occlusions. Overall, pretraining on coarse labels prior to detection makes it possible to rely on weakly-supervised pretrained weights even when labeled data are limited, achieving results comparable to generic-weight initialization.
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Computer Vision
SelfieAvatar: Real-time Head Avatar reenactment from a Selfie Video
Wei Liang, Hui Yu, Derui Ding et al. (+2 more)
Head avatar reenactment focuses on creating animatable personal avatars from
monocular videos, serving as a foundational element for applications like social signal understanding, gaming, human-machine interaction, and computer
vision. Recent advances in 3D Morphable Model (3DMM)-based facial reconstruction methods have achieved remarkable high-fidelity face estimation. However, on the one hand, they struggle to capture the entire head, including non-facial regions and background details in real time, which is an essential aspect for producing realistic, high-fidelity head avatars. On the other hand, recent approaches leveraging generative
adversarial networks (GANs) for head avatar generation from
videos can achieve high-quality reenactments but encounter limitations in reproducing fine-grained head details, such as wrinkles and hair textures. In addition, existing methods generally rely on a large amount of training data, and rarely focus on using only a simple selfie
video to achieve avatar reenactment. To address these challenges, this study introduces a method for detailed head avatar reenactment using a selfie
video. The approach combines 3DMMs with a StyleGAN-based generator. A detailed reconstruction model is proposed, incorporating mixed loss functions for foreground reconstruction and avatar image generation during
adversarial training to recover high-frequency details. Qualitative and quantitative evaluations on self-reenactment and cross-reenactment tasks demonstrate that the proposed method achieves superior head avatar reconstruction with rich and intricate textures compared to existing approaches.
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All authors Wei Liang, Hui Yu, Derui Ding, Rachael E. Jack, Philippe G. Schyns
Computer Vision
Audio-Driven Talking Face Generation with Blink Embedding and Hash Grid Landmarks Encoding
Yuhui Zhang, Hui Yu, Wei Liang et al. (+1 more)
Dynamic Neural Radiance Fields (NeRF) have demonstrated considerable success in generating high-fidelity 3D models of talking portraits. Despite significant advancements in the rendering speed and generation quality, challenges persist in accurately and efficiently capturing mouth movements in talking portraits. To tackle this challenge, we propose an automatic method based on blink
embedding and hash grid landmarks encoding in this study, which can substantially enhance the fidelity of talking faces. Specifically, we leverage facial features encoded as conditional features and integrate audio features as residual terms into our model through a Dynamic Landmark
Transformer. Furthermore, we employ neural radiance fields to model the entire face, resulting in a lifelike face representation. Experimental evaluations have validated the superiority of our approach to existing methods.
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All authors Yuhui Zhang, Hui Yu, Wei Liang, Sunjie Zhang
Computer Vision
Dynamic Mask-Based Backdoor Attack Against Vision AI Models: A Case Study on Mushroom Detection
Zeineb Dridi, Jihen Bennaceur and Amine Ben Hassouna
Deep learning has revolutionized numerous tasks within the computer
vision field, including image classification, image
segmentation, and object detection. However, the increasing deployment of deep learning models has exposed them to various
adversarial attacks, including
backdoor attacks. This paper presents a novel dynamic mask-based
backdoor attack method, specifically designed for object detection models. We exploit a
dataset poisoning technique to embed a malicious trigger, rendering any models trained on this compromised
dataset vulnerable to our
backdoor attack. We particularly focus on a mushroom detection
dataset to demonstrate the practical risks posed by such attacks on critical real-life domains. Our work also emphasizes the importance of creating a detailed
backdoor attack scenario to illustrate the significant risks associated with the outsourcing practice. Our approach leverages SAM, a recent and powerful image
segmentation AI model, to create masks for dynamic trigger placement, introducing a new and stealthy attack method. Through extensive experimentation, we show that our sophisticated attack scenario maintains high accuracy on clean data with the YOLOv7 object detection model while achieving high attack success rates on poisoned samples. Our approach surpasses traditional methods for
backdoor injection, which are based on static and consistent patterns. Our findings underscore the urgent need for robust countermeasures to protect deep learning models from these evolving
adversarial threats.
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Computer Vision · Cross-listed from cs.CR
GUIGuard-Bench: Toward a General Evaluation for Privacy-Preserving GUI Agents
Yanxi Wang, Zhiling Zhang, Wenbo Zhou et al. (+6 more)
As GUI
agents increasingly rely on screenshots to perceive and operate digital environments, they may inadvertently expose sensitive information such as identities, accounts, locations, and behavioral traces. While existing
benchmarks primarily focus on task completion, grounding, or defenses against third-party attacks, current visual
privacy datasets remain largely restricted to static natural images, limiting their ability to capture the contextual dependence and task relevance of
privacy risks in GUI task trajectories. To bridge this gap, we introduce GUIGuard-Bench, a first-step
benchmark for studying
privacy-preserving GUI
agents in trajectory-based GUI workflows. GUIGuard-Bench contains 241 real GUI-
agent trajectories with 4,080 screenshots across Android and PC environments. Each screenshot is annotated at the region level with
privacy bounding boxes, semantic
privacy categories, risk levels, and whether the private information is necessary for completing the task. Built on these annotations, GUIGuard-Bench supports three complementary evaluations:
privacy recognition, offline planning fidelity under protected screenshots, and the utility impact of different protection strategies. Our results show that current models can often detect whether a screenshot contains private information, but they struggle with fine-grained localization, category recognition, risk assessment, and task-necessity judgment. We also find that closed-source models, exemplified by Claude Sonnet 4.6, can maintain largely consistent planner semantics in Android environments after
privacy protection is applied. Our results highlight
privacy recognition as a critical bottleneck for practical GUI
agents. Project: https://futuresis.github.io/GUIGuard-page/
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All authors Yanxi Wang, Zhiling Zhang, Wenbo Zhou, Weiming Zhang, Jie Zhang, Qiannan Zhu, Yu Shi, Shuxin Zheng, Jiyan He
Computer Vision
SeNeDiF-OOD: Semantic Nested Dichotomy Fusion for Out-of-Distribution Detection Methodology in Open-World Classification. A Case Study on Monument Style Classification
Ignacio Antequera-Sánchez, Juan Luis Suárez-Díaz, Rosana Montes et al. (+1 more)
Out-of-distribution (OOD) detection is a fundamental requirement for the reliable deployment of artificial intelligence applications in open-world environments. However, addressing the heterogeneous nature of OOD data, ranging from low-level corruption to semantic shifts, remains a complex challenge that single-stage detectors often fail to resolve. To address this issue, we propose SeNeDiF-OOD, a novel methodology based on Semantic Nested Dichotomy Fusion. This framework decomposes the detection task into a hierarchical structure of binary fusion nodes, where each layer is designed to integrate decision boundaries aligned with specific levels of semantic abstraction. To validate the proposed framework, we present a comprehensive case study using MonuMAI, a real-world architectural style recognition system exposed to an open environment. This application faces a diverse range of inputs, including non-monument images, unknown architectural styles, and
adversarial attacks, making it an ideal testbed for our proposal. Through extensive experimental evaluation in this domain, results demonstrate that our hierarchical fusion methodology significantly outperforms traditional baselines, effectively filtering these diverse OOD categories while preserving in-distribution performance.
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All authors Ignacio Antequera-Sánchez, Juan Luis Suárez-Díaz, Rosana Montes, Francisco Herrera
Computer Vision · Cross-listed from cs.RO
Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge
Li Kang, Heng Zhou, Xiufeng Song et al. (+41 more)
Recent advancements in
multimodal large
language models and
vision-languageaction models have significantly driven progress in
Embodied AI. As the field transitions toward more complex task scenarios, multi-
agent system frameworks are becoming essential for achieving scalable, efficient, and collaborative solutions. This shift is fueled by three primary factors: increasing
agent capabilities, enhancing system efficiency through task delegation, and enabling advanced human-
agent interactions. To address the challenges posed by multi-
agent collaboration, we propose the Multi-
Agent Robotic System (MARS) Challenge, held at the NeurIPS 2025 Workshop on SpaVLE. The competition focuses on two critical areas: planning and control, where participants explore multi-
agent embodied planning using
vision-
language models (VLMs) to coordinate tasks and policy execution to perform robotic manipulation in dynamic environments. By evaluating solutions submitted by participants, the challenge provides valuable insights into the design and coordination of
embodied multi-
agent systems, contributing to the future development of advanced collaborative AI systems.
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All authors Li Kang, Heng Zhou, Xiufeng Song, Rui Li, Bruno N.Y. Chen, Ziye Wang, Ximeng Meng, Stone Tao, Yiran Qin, Xiaohong Liu, Ruimao Zhang, Lei Bai, Yilun Du, Hao Su, Philip Torr, Zhenfei Yin, Ruihao Gong, Yejun Zeng, Fengjun Zhong, Shenghao Jin, Jinyang Guo, Xianglong Liu, Xiaojun Jia, Tianqi Shan, Wenqi Ren, Simeng Qin, Jialing Yang, Xiaoyu Ma, Tianxing Chen, Zixuan Li, Zijian Cai, Yan Qin, Yusen Qin, Qiangyu Chen, Kaixuan Wang, Zhaoming Han, Yao Mu, Ping Luo, Yuanqi Yao, Haoming Song, Jan-Nico Zaech, Fabien Despinoy, Danda Pani Paudel, Luc Van Gool
Computer Vision
Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning
Judith Vilella-Cantos, Mauro Martini, Marcello Chiaberge et al. (+2 more)
Localization in agricultural environments is challenging due to their unstructured nature and lack of distinctive landmarks. Although agricultural settings have been studied in the context of object classification and
segmentation, the place recognition task for mobile
robots is not trivial in the current state of the art. In this study, we propose MinkUNeXt-VINE, a lightweight, deep-learning-based method that surpasses state-of-the-art methods in vineyard environments thanks to its pre-processing and Matryoshka Representation Learning multi-loss approach. Our method prioritizes enhanced performance with low-cost, sparse
LiDAR inputs and lower-dimensionality outputs to ensure high efficiency in real-time scenarios. Additionally, we present a comprehensive ablation study of the results on various evaluation cases and two extensive long-term vineyard
datasets employing different
LiDAR sensors. The results demonstrate the efficiency of the trade-off output produced by this approach, as well as its robust performance on low-cost and low-resolution input data. The code is publicly available for reproduction.
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All authors Judith Vilella-Cantos, Mauro Martini, Marcello Chiaberge, Mónica Ballesta, David Valiente
Computer Vision · Cross-listed from cs.LG
SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model
Jan Hagnberger and Mathias Niepert
Machine learning-based surrogate models have emerged as more efficient alternatives to numerical solvers for physical simulations over complex geometries, such as car bodies. Many existing models incorporate the simulation mesh as an additional input, thereby reducing prediction errors. However, generating a simulation mesh for new geometries is computationally costly. In contrast, mesh-free methods, which do not rely on the simulation mesh, typically incur higher errors. Motivated by these considerations, we introduce SMART, a neural surrogate model that predicts physical quantities at arbitrary query locations using only a point-cloud representation of the geometry, without requiring access to the simulation mesh. The geometry and simulation parameters are encoded into a shared latent space that captures both structural and parametric characteristics of the physical field. A physics decoder then attends to the encoder's intermediate latent representations to map spatial queries to physical quantities. Through this cross-layer interaction, the model jointly updates latent
geometric features and the evolving physical field. Extensive experiments show that SMART is competitive with and often outperforms existing methods that rely on the simulation mesh as input, demonstrating its capabilities for industry-level simulations.
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Computer Vision
Are Video Generation Models Geographically Fair? An Attraction-Centric Evaluation of Global Visual Knowledge
Xiao Liu and Jiawei Zhang
Recent advances in text-to-
video generation have produced visually compelling results, yet it remains unclear whether these models encode geographically equitable visual knowledge. In this work, we investigate the geo-equity and geographically grounded visual knowledge of text-to-
video models through an attraction-centric evaluation. We introduce Geo-Attraction Landmark Probing (GAP), a systematic framework for assessing how faithfully models synthesize tourist attractions from diverse regions, and construct GEOATTRACTION-500, a
benchmark of 500 globally distributed attractions spanning varied regions and popularity levels. GAP integrates complementary metrics that disentangle overall
video quality from attraction-specific knowledge, including global structural
alignment, fine-grained keypoint-based
alignment, and
vision-
language model judgments, all validated against human evaluation. Applying GAP to the state-of-the-art text-to-
video model Sora 2, we find that, contrary to common assumptions of strong geographic bias, the model exhibits a relatively uniform level of geographically grounded visual knowledge across regions, development levels, and cultural groupings, with only weak dependence on attraction popularity. These results suggest that current text-to-
video models express global visual knowledge more evenly than expected, highlighting both their promise for globally deployed applications and the need for continued evaluation as such systems evolve.
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Computer Vision · Cross-listed from cs.RO
A Pragmatic VLA Foundation Model
Wei Wu, Fan Lu, Yunnan Wang et al. (+22 more)
Offering great potential in robotic manipulation, a capable
Vision-
Language-Action (VLA) foundation model is expected to faithfully generalize across tasks and platforms while ensuring cost efficiency (e.g., data and GPU hours required for adaptation). To this end, we develop LingBot-VLA with around 20,000 hours of real-world data from 9 popular dual-arm
robot configurations. Through a systematic assessment on 4 robotic platforms, each completing 100 tasks with 130 post-training episodes per task, our model achieves clear superiority over competitors, showcasing its strong performance and broad generalizability. We have also built an efficient codebase, which delivers a throughput of 261 samples per second with an 8-GPU training setup, representing a 1.5~2.8$\times$ (depending on the relied VLM base model) speedup over existing VLA-oriented codebases. The above features ensure that our model is well-suited for real-world deployment. To advance the field of
robot learning, we provide open access to the code, base model, and
benchmark data, with a focus on enabling more challenging tasks and promoting sound evaluation standards.
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All authors Wei Wu, Fan Lu, Yunnan Wang, Shuai Yang, Shi Liu, Fangjing Wang, Qian Zhu, He Sun, Yong Wang, Shuailei Ma, Yiyu Ren, Kejia Zhang, Hui Yu, Jingmei Zhao, Shuai Zhou, Zhenqi Qiu, Houlong Xiong, Ziyu Wang, Zechen Wang, Ran Cheng, Yong-Lu Li, Yongtao Huang, Xing Zhu, Yujun Shen, Kecheng Zheng
Computer Vision · Cross-listed from cs.LG
Counterfactual Explanations on Robust Perceptual Geodesics
Eslam Zaher, Maciej Trzaskowski, Quan Nguyen et al. (+1 more)
Latent-space optimization methods for counterfactual explanations - framed as minimal semantic perturbations that change model predictions - inherit the ambiguity of Wachter et al.'s objective: the choice of distance metric dictates whether perturbations are meaningful or
adversarial. Existing approaches adopt flat or misaligned geometries, leading to off-
manifold artifacts, semantic drift, or
adversarial collapse. We introduce Perceptual Counterfactual Geodesics (PCG), a method that constructs counterfactuals by tracing geodesics under a perceptually Riemannian metric induced from robust
vision features. This geometry aligns with human perception and penalizes brittle directions, enabling smooth, on-
manifold, semantically valid transitions. Experiments on three
vision datasets show that PCG outperforms baselines and reveals failure modes hidden under standard metrics.
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All authors Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta
Computer Vision
Splat-Portrait: Generalizing Talking Heads with Gaussian Splatting
Tong Shi, Melonie de Almeida, Daniela Ivanova et al. (+2 more)
Talking Head Generation aims at synthesizing natural-looking talking
videos from
speech and a single portrait image. Previous 3D talking head generation methods have relied on domain-specific heuristics such as warping-based facial motion representation priors to animate talking motions, yet still produce inaccurate 3D avatar reconstructions, thus undermining the realism of generated animations. We introduce Splat-Portrait, a Gaussian-splatting-based method that addresses the challenges of 3D head reconstruction and lip motion synthesis. Our approach automatically learns to disentangle a single portrait image into a static 3D reconstruction represented as static Gaussian Splatting, and a predicted whole-image 2D background. It then generates natural lip motion conditioned on input audio, without any motion driven priors. Training is driven purely by 2D reconstruction and score-
distillation losses, without 3D supervision nor landmarks. Experimental results demonstrate that Splat-Portrait exhibits superior performance on talking head generation and novel view synthesis, achieving better visual quality compared to previous works. Our project code and supplementary documents are public available at https://github.com/stonewalking/Splat-portrait.
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All authors Tong Shi, Melonie de Almeida, Daniela Ivanova, Nicolas Pugeault, Paul Henderson
Computer Vision · Cross-listed from cs.AI
AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning
Mingyang Song, Haoyu Sun, Jiawei Gu et al. (+4 more)
When humans face problems beyond their immediate capabilities, they rely on tools, providing a promising paradigm for improving visual
reasoning in
multimodal large
language models (MLLMs). Effective
reasoning, therefore, hinges on knowing which tools to use, when to invoke them, and how to compose them over multiple steps, even when faced with new tools or new tasks. We introduce AdaReasoner, a family of
multimodal models that learn tool use as a general
reasoning skill rather than as tool-specific or explicitly supervised behavior. AdaReasoner is enabled by (i) a scalable data curation pipeline exposing models to long-horizon, multi-step tool interactions; (ii) Tool-GRPO, a reinforcement learning
algorithm that optimizes tool selection and sequencing based on end-task success; and (iii) an adaptive learning mechanism that dynamically regulates tool usage. Together, these components allow models to infer tool utility from task context and intermediate outcomes, enabling coordination of multiple tools and generalization to unseen tools. Empirically, AdaReasoner exhibits strong tool-adaptive and generalization behaviors: it autonomously adopts beneficial tools, suppresses irrelevant ones, and adjusts tool usage frequency based on task demands, despite never being explicitly trained to do so. These capabilities translate into state-of-the-art performance across challenging
benchmarks, improving the 7B base model by +24.9% on average and surpassing strong proprietary systems such as GPT-5 on multiple tasks, including VSP and Jigsaw.
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All authors Mingyang Song, Haoyu Sun, Jiawei Gu, Linjie Li, Luxin Xu, Ranjay Krishna, Yu Cheng
Computer Vision
CONQUER: Context-Aware Representation with Query Enhancement for Text-Based Person Search
Zequn Xie
Text-Based Person Search (TBPS) aims to retrieve pedestrian images from large galleries using natural
language descriptions. This task, essential for public safety applications, is hindered by cross-modal discrepancies and ambiguous user queries. We introduce CONQUER, a two-stage framework designed to address these challenges by enhancing cross-modal
alignment during training and adaptively refining queries at inference. During training, CONQUER employs multi-granularity encoding, complementary pair mining, and context-guided optimal matching based on Optimal Transport to learn robust
embeddings. At inference, a plug-and-play query enhancement module refines vague or incomplete queries via anchor selection and attribute-driven enrichment, without requiring retraining of the backbone. Extensive experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid demonstrate that CONQUER consistently outperforms strong baselines in both Rank-1 accuracy and mAP, yielding notable improvements in cross-domain and incomplete-query scenarios. These results highlight CONQUER as a practical and effective solution for real-world TBPS deployment. Source code is available at https://github.com/zqxie77/CONQUER.
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Computer Vision
Adaptive Domain Shift in Diffusion Models for Cross-Modality Image Translation
Zihao Wang, Yuzhou Chen and Shaogang Ren
Cross-modal image translation remains brittle and inefficient. Standard
diffusion approaches often rely on a single, global linear transfer between domains. We find that this shortcut forces the sampler to traverse off-
manifold, high-cost regions, inflating the correction burden and inviting semantic drift. We refer to this shared failure mode as fixed-schedule domain transfer. In this paper, we embed domain-shift dynamics directly into the generative process. Our model predicts a spatially varying mixing field at every reverse step and injects an explicit, target-consistent restoration term into the drift. This in-step guidance keeps large updates on-
manifold and shifts the model's role from global
alignment to local residual correction. We provide a continuous-time formulation with an exact solution form and derive a practical first-order sampler that preserves marginal consistency. Empirically, across translation tasks in medical imaging, remote sensing, and electroluminescence semantic mapping, our framework improves structural fidelity and semantic consistency while converging in fewer denoising steps.
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Computer Vision
Scale-Aware Self-Supervised Learning for Segmentation of Small and Sparse Structures
Jorge Quesada and Ghassan AlRegib
Self-supervised learning (SSL) has emerged as a powerful strategy for representation learning under limited annotation regimes, yet its effectiveness remains highly sensitive to many factors, especially the nature of the target task. In
segmentation, existing pipelines are typically tuned to large, homogeneous regions, but their performance drops when objects are small, sparse, or locally irregular. In this work, we propose a scale-aware SSL adaptation that integrates small-window cropping into the augmentation pipeline, zooming in on fine-scale structures during pretraining. We evaluate this approach across two domains with markedly different data modalities: seismic imaging, where the goal is to segment sparse faults, and neuroimaging, where the task is to delineate small cellular structures. In both settings, our method yields consistent improvements over standard and state-of-the-art baselines under label constraints, improving accuracy by up to 13% for fault
segmentation and 5% for
cell delineation. In contrast, large-scale features such as seismic facies or tissue regions see little benefit, underscoring that the value of SSL depends critically on the scale of the target objects. Our findings highlight the need to align SSL design with object size and sparsity, offering a general principle for buil ding more effective representation learning pipelines across scientific imaging domains.
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Computer Vision · Cross-listed from cs.CR
Multimodal Privacy-Preserving Entity Resolution with Fully Homomorphic Encryption
Susim Roy and Nalini Ratha
The canonical challenge of entity resolution within high-compliance sectors, where secure identity reconciliation is frequently confounded by significant data heterogeneity, including syntactic variations in personal identifiers, is a longstanding and complex problem. To this end, we introduce a novel
multimodal framework operating with the voluminous data sets typical of government and financial institutions. Specifically, our methodology is designed to address the tripartite challenge of data volume, matching fidelity, and
privacy. Consequently, the underlying plaintext of personally identifiable information remains computationally inaccessible throughout the matching lifecycle, empowering institutions to rigorously satisfy stringent regulatory mandates with cryptographic assurances of client confidentiality while achieving a demonstrably low equal error rate and maintaining computational tractability at scale.
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Computer Vision
EFSI-DETR: Efficient Frequency-Semantic Integration for Real-Time Small Object Detection in UAV Imagery
Yu Xia, Chang Liu, Tianqi Xiang et al. (+1 more)
Real-time small object detection in Unmanned Aerial Vehicle (
UAV) imagery remains challenging due to limited feature representation and ineffective multi-scale fusion. Existing methods underutilize frequency information and rely on static convolutional operations, which constrain the capacity to obtain rich feature representations and hinder the effective exploitation of deep semantic features. To address these issues, we propose EFSI-DETR, a novel detection framework that integrates efficient semantic feature enhancement with dynamic frequency-spatial guidance. EFSI-DETR comprises two main components: (1) a Dynamic Frequency-Spatial Unified Synergy Network (DyFusNet) that jointly exploits frequency and spatial cues for robust multi-scale feature fusion, (2) an Efficient Semantic Feature Concentrator (ESFC) that enables deep semantic extraction with minimal computational cost. Furthermore, a Fine-grained Feature Retention (FFR) strategy is adopted to incorporate spatially rich shallow features during fusion to preserve fine-grained details, crucial for small object detection in
UAV imagery. Extensive experiments on VisDrone and CODrone
benchmarks demonstrate that our EFSI-DETR achieves the state-of-the-art performance with real-time efficiency, yielding improvement of 1.6% and 5.8% in AP and AP$_{s}$ on VisDrone, while obtaining 188 FPS inference speed on a single RTX 4090 GPU.
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All authors Yu Xia, Chang Liu, Tianqi Xiang, Zhigang Tu
Computer Vision
AGSP-DSA: An Adaptive Graph Signal Processing Framework for Robust Multimodal Fusion with Dynamic Semantic Alignment
KV Karthikeya, Ashok Kumar Das, Shantanu Pal et al. (+2 more)
In this paper, we introduce an Adaptive Graph Signal Processing with Dynamic Semantic
Alignment (AGSP DSA) framework to perform robust
multimodal data fusion over heterogeneous sources, including text, audio, and images. The requested approach uses a dual-graph construction to learn both intra-modal and inter-modal relations, spectral graph filtering to boost the informative signals, and effective node
embedding with Multi-scale Graph Convolutional Networks (GCNs). Semantic aware
attention mechanism: each modality may dynamically contribute to the context with respect to contextual relevance. The experimental outcomes on three
benchmark datasets, including CMU-MOSEI, AVE, and MM-IMDB, show that AGSP-DSA performs as the state of the art. More precisely, it achieves 95.3% accuracy, 0.936 F1-score, and 0.924 mAP on CMU-MOSEI, improving MM-GNN by 2.6 percent in accuracy. It gets 93.4% accuracy and 0.911 F1-score on AVE and 91.8% accuracy and 0.886 F1-score on MM-IMDB, which demonstrate good generalization and robustness in the missing modality setting. These findings verify the efficiency of AGSP-DSA in promoting
multimodal learning in sentiment analysis, event recognition and multimedia classification.
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All authors KV Karthikeya, Ashok Kumar Das, Shantanu Pal, Vivekananda Bhat K, Arun Sekar Rajasekaran